ML Anthology

An Iterative Improvement Procedure for Hierarchical Clustering

David Kauchak, Sanjoy Dasgupta
NeurIPS 2003 pp. 481-488
/neurips/2003/kauchak2003neurips-iterative/

Abstract

We describe a procedure which finds a hierarchical clustering by hill- climbing. The cost function we use is a hierarchical extension of the k-means cost; our local moves are tree restructurings and node reorder- ings. We show these can be accomplished efficiently, by exploiting spe- cial properties of squared Euclidean distances and by using techniques from scheduling algorithms.

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Cite

Text

Kauchak and Dasgupta. "An Iterative Improvement Procedure for Hierarchical Clustering." Neural Information Processing Systems, 2003.

Markdown

[Kauchak and Dasgupta. "An Iterative Improvement Procedure for Hierarchical Clustering." Neural Information Processing Systems, 2003.](https://mlanthology.org/neurips/2003/kauchak2003neurips-iterative/)

BibTeX

@inproceedings{kauchak2003neurips-iterative,
  title     = {{An Iterative Improvement Procedure for Hierarchical Clustering}},
  author    = {Kauchak, David and Dasgupta, Sanjoy},
  booktitle = {Neural Information Processing Systems},
  year      = {2003},
  pages     = {481-488},
  url       = {https://mlanthology.org/neurips/2003/kauchak2003neurips-iterative/}
}